Classical methods of measuring biometrical signals or vital signs, such as heart rate, respiratory rate or blood oxygen saturation, require the user to wear annoying body sensors, which might be experienced as obtrusive to normal human life activity.
One solution to this problem is photoplethysmography imaging (PPG) which allows remote contactless monitoring of vital signs. PPG is based on the principle that temporal variations in blood volume in the skin lead to variations in light absorptions by the skin. Such variations can be registered by a video camera that takes images of an area of bare skin, for example the face. By looking at periodic variations of the intensity signal, e.g. the RGB values of a group of pixels from the video camera, the heart rate and respiratory rate can be extracted. However, as this method evaluates light coming from the target, any change in illumination conditions or a movement of the subject will create additional disturbances in a temporal signal. Such a disturbance signal can be measured by means of dedicated video processing algorithms applied to the video stream from a vital signs camera. The paper by Schmitz “Video Camera based Photoplethysmography using Ambient Light” (Graduation Symposium at the Technical University of Eindhoven, 2011), suggests that motion vectors can be used to track pixels containing heart rate information.
However, the measurement of a disturbance signal requires complex algorithms and increases the hardware requirements of a vital signs camera, such as higher resolution or better sharpness with the associated high-quality optics. Moreover, such video-based motion estimation and tracking in a high resolution video stream requires extensive signal processing with costly hardware.